changed format of columns

app.py

@@ -5,7 +5,8 @@ import joblib
 def create_dataframe_from_user_input():
     """
     Collects user input for house features using Streamlit and
-    returns a Pandas DataFrame.
+    returns a Pandas DataFrame.  The input fields are organized
+    into categorized sections for better usability.
     """
 
     # Define the lists of possible values for dropdown selections
@@ -24,7 +25,7 @@ def create_dataframe_from_user_input():
     flood_zone_type_list = ['NON_FLOOD_ZONE', 'POSSIBLE_FLOOD_ZONE', 'RECOGNIZED_FLOOD_ZONE',
                             'RECOGNIZED_N_CIRCUMSCRIBED_FLOOD_ZONE', 'CIRCUMSCRIBED_WATERSIDE_ZONE',
                             'CIRCUMSCRIBED_FLOOD_ZONE', 'POSSIBLE_N_CIRCUMSCRIBED_FLOOD_ZONE',
-                            'POSSIBLE_N_CIRCUMSCRIBED_WATERSIDE_ZONE', 'RECOGNIZED_N_CIRCUMSCRIBED_WATERSIDE_FLOOD_ZONE']
+                            'POSSIBLE_N_CIRCUMSCRIBED_WATERSIDE_ZONE', 'RECOGNIZED_N_CIRCUMSCRIBED_WATERSIDE_ZONE']
     heating_type_list = ['GAS', 'FUELOIL', 'ELECTRIC', 'PELLET', 'WOOD', 'SOLAR', 'CARBON']
     kitchen_type_list = ['INSTALLED', 'HYPER_EQUIPPED', 'SEMI_EQUIPPED', 'NOT_INSTALLED',
                          'USA_HYPER_EQUIPPED', 'USA_INSTALLED', 'USA_SEMI_EQUIPPED',
@@ -38,63 +39,87 @@ def create_dataframe_from_user_input():
     # Create Streamlit input fields
     st.header("Enter House Information")
 
-    col1, col2 = st.columns(2) # Divide the layout in two columns
-
+    # --- Property Details ---
+    st.subheader("Property Details")
+    col1, col2 = st.columns(2)
     with col1:
         property_type = st.selectbox("Property Type", type_list, key='type')
         property_subtype = st.selectbox("Subtype", subtype_list, key='subtype')
-        bedroom_count = st.number_input("Bedroom Count", min_value=0, step=1, key='bedroomCount')
-        bathroom_count = st.number_input("Bathroom Count", min_value=0, step=1, key='bathroomCount')
         province = st.selectbox("Province", province_list, key='province')
         locality = st.text_input("Locality", key='locality')
         post_code = st.number_input("Post Code", min_value=1000, max_value=9999, step=1, key='postCode')
-        habitable_surface = st.number_input("Habitable Surface (sqm)", min_value=0.0, key='habitableSurface')
-        room_count = st.number_input("Room Count", min_value=0, step=1, key='roomCount')
-        monthly_cost = st.number_input("Monthly Cost (€)", min_value=0.0, key='monthlyCost')
-        has_attic = st.selectbox("Has Attic", ['Yes', 'No'], key='hasAttic')
-        has_basement = st.selectbox("Has Basement", ['Yes', 'No'], key='hasBasement')
-        has_dressing_room = st.selectbox("Has Dressing Room", ['Yes', 'No'], key='hasDressingRoom')
-        dining_room_surface = st.number_input("Dining Room Surface (sqm)", min_value=0.0, key='diningRoomSurface')
-        has_dining_room = st.selectbox("Has Dining Room", ['Yes', 'No'], key='hasDiningRoom')
+    with col2:
         building_condition = st.selectbox("Building Condition", building_condition_list, key='buildingCondition')
         building_construction_year = st.number_input("Building Construction Year", min_value=1000, max_value=2024, step=1, key='buildingConstructionYear')
         facade_count = st.number_input("Facade Count", min_value=0, step=1, key='facadeCount')
         floor_count = st.number_input("Floor Count", min_value=0, step=1, key='floorCount')
-        street_facade_width = st.number_input("Street Facade Width (m)", min_value=0.0, key='streetFacadeWidth')
-        has_lift = st.selectbox("Has Lift", ['Yes', 'No'], key='hasLift')
         flood_zone_type = st.selectbox("Flood Zone Type", flood_zone_type_list, key='floodZoneType')
-        heating_type = st.selectbox("Heating Type", heating_type_list, key='heatingType')
-        has_heat_pump = st.selectbox("Has Heat Pump", ['Yes', 'No'], key='hasHeatPump')
+        epc_score = st.selectbox("EPC Score", epc_score_list, key='epcScore')
 
-    with col2:
-        has_photovoltaic_panels = st.selectbox("Has Photovoltaic Panels", ['Yes', 'No'], key='hasPhotovoltaicPanels')
-        has_thermic_panels = st.selectbox("Has Thermic Panels", ['Yes', 'No'], key='hasThermicPanels')
+    # --- Room Information ---
+    st.subheader("Room Information")
+    col3, col4 = st.columns(2)
+    with col3:
+        bedroom_count = st.number_input("Bedroom Count", min_value=0, step=1, key='bedroomCount')
+        bathroom_count = st.number_input("Bathroom Count", min_value=0, step=1, key='bathroomCount')
+        room_count = st.number_input("Room Count", min_value=0, step=1, key='roomCount')
+        has_attic = st.selectbox("Has Attic", ['Yes', 'No'], key='hasAttic')
+        has_basement = st.selectbox("Has Basement", ['Yes', 'No'], key='hasBasement')
+        has_dressing_room = st.selectbox("Has Dressing Room", ['Yes', 'No'], key='hasDressingRoom')
+        has_dining_room = st.selectbox("Has Dining Room", ['Yes', 'No'], key='hasDiningRoom')
+        dining_room_surface = st.number_input("Dining Room Surface (sqm)", min_value=0.0, key='diningRoomSurface')
+    with col4:
+        has_living_room = st.selectbox("Has Living Room", ['Yes', 'No'], key='hasLivingRoom')
+        living_room_surface = st.number_input("Living Room Surface (sqm)", min_value=0.0, key='livingRoomSurface')
         kitchen_surface = st.number_input("Kitchen Surface (sqm)", min_value=0.0, key='kitchenSurface')
         kitchen_type = st.selectbox("Kitchen Type", kitchen_type_list, key='kitchenType')
+        toilet_count = st.number_input("Toilet Count", min_value=0, step=1, key='toiletCount')
+        has_office = st.selectbox("Has Office", ['Yes', 'No'], key='hasOffice')
+        has_lift = st.selectbox("Has Lift", ['Yes', 'No'], key='hasLift')
+
+    # --- Surface Areas ---
+    st.subheader("Surface Areas")
+    col5, col6 = st.columns(2)
+    with col5:
+        habitable_surface = st.number_input("Habitable Surface (sqm)", min_value=0.0, key='habitableSurface')
         land_surface = st.number_input("Land Surface (sqm)", min_value=0.0, key='landSurface')
-        has_living_room = st.selectbox("Has Living Room", ['Yes', 'No'], key='hasLivingRoom')
-        living_room_surface = st.number_input("Living Room Surface (sqm)", min_value=0.0, key='livingRoomSurface')
-        has_balcony = st.selectbox("Has Balcony", ['Yes', 'No'], key='hasBalcony')
-        has_garden = st.selectbox("Has Garden", ['Yes', 'No'], key='hasGarden')
         garden_surface = st.number_input("Garden Surface (sqm)", min_value=0.0, key='gardenSurface')
+    with col6:
+        terrace_surface = st.number_input("Terrace Surface (sqm)", min_value=0.0, key='terraceSurface')
+        street_facade_width = st.number_input("Street Facade Width (m)", min_value=0.0, key='streetFacadeWidth')
+        monthly_cost = st.number_input("Monthly Cost (€)", min_value=0.0, key='monthlyCost')
+
+    # --- Outdoor Features ---
+    st.subheader("Outdoor Features")
+    col7, col8 = st.columns(2)
+    with col7:
+        has_garden = st.selectbox("Has Garden", ['Yes', 'No'], key='hasGarden')
         garden_orientation = st.selectbox("Garden Orientation", garden_orientation_list, key='gardenOrientation')
+        has_balcony = st.selectbox("Has Balcony", ['Yes', 'No'], key='hasBalcony')
+        has_terrace = st.selectbox("Has Terrace", ['Yes', 'No'], key='hasTerrace')
+        terrace_orientation = st.selectbox("Terrace Orientation", terrace_orientation_list, key='terraceOrientation')
+    with col8:
         parking_count_indoor = st.number_input("Indoor Parking Count", min_value=0, step=1, key='parkingCountIndoor')
         parking_count_outdoor = st.number_input("Outdoor Parking Count", min_value=0, step=1, key='parkingCountOutdoor')
+        has_swimming_pool = st.selectbox("Has Swimming Pool", ['Yes', 'No'], key='hasSwimmingPool')
+
+    # --- Additional Features ---
+    st.subheader("Additional Features")
+    col9, col10 = st.columns(2)
+    with col9:
+        heating_type = st.selectbox("Heating Type", heating_type_list, key='heatingType')
+        has_heat_pump = st.selectbox("Has Heat Pump", ['Yes', 'No'], key='hasHeatPump')
+        has_photovoltaic_panels = st.selectbox("Has Photovoltaic Panels", ['Yes', 'No'], key='hasPhotovoltaicPanels')
+        has_thermic_panels = st.selectbox("Has Thermic Panels", ['Yes', 'No'], key='hasThermicPanels')
+    with col10:
         has_air_conditioning = st.selectbox("Has Air Conditioning", ['Yes', 'No'], key='hasAirConditioning')
         has_armored_door = st.selectbox("Has Armored Door", ['Yes', 'No'], key='hasArmoredDoor')
         has_visiophone = st.selectbox("Has Visiophone", ['Yes', 'No'], key='hasVisiophone')
-        has_office = st.selectbox("Has Office", ['Yes', 'No'], key='hasOffice')
-        toilet_count = st.number_input("Toilet Count", min_value=0, step=1, key='toiletCount')
-        has_swimming_pool = st.selectbox("Has Swimming Pool", ['Yes', 'No'], key='hasSwimmingPool')
         has_fireplace = st.selectbox("Has Fireplace", ['Yes', 'No'], key='hasFireplace')
-        has_terrace = st.selectbox("Has Terrace", ['Yes', 'No'], key='hasTerrace')
-        terrace_surface = st.number_input("Terrace Surface (sqm)", min_value=0.0, key='terraceSurface')
-        terrace_orientation = st.selectbox("Terrace Orientation", terrace_orientation_list, key='terraceOrientation')
         accessible_disabled_people = st.selectbox("Accessible Disabled People", ['True', 'False'], key='accessibleDisabledPeople')
-        epc_score = st.selectbox("EPC Score", epc_score_list, key='epcScore')
 
     # Create a button to trigger DataFrame creation
-    if st.button("Predict"): # Changed button text to "Predict"
+    if st.button("Predict"):
         # Create the DataFrame
         data = {
             'type': property_type,
@@ -147,9 +172,7 @@ def create_dataframe_from_user_input():
             'accessibleDisabledPeople': accessible_disabled_people == 'True',
             'epcScore': epc_score
         }
-        df = pd.DataFrame(data, index=[0]) # Important : wrap data in a list.
-        #st.subheader("Generated DataFrame")
-        #st.dataframe(df)
+        df = pd.DataFrame(data, index=[0])
         pipeline = joblib.load('saved/pipeline.pkl')
         model = joblib.load('saved/model.pkl')
         expected_columns = joblib.load('saved/columns.pkl')
@@ -161,8 +184,8 @@ def create_dataframe_from_user_input():
         df_test = df_test[expected_columns]
         preds = model.predict(df_test)
         st.subheader("Price prediction")
-        st.write("House price : ", preds)
-        return df # Return the dataframe.
+        st.markdown(f"<h1 style='text-align: center; color: red;'>{preds[0]:.2f} €</h1>", unsafe_allow_html=True)
+        return df
 
 if __name__ == "__main__":
     create_dataframe_from_user_input()